Chapter 2 Projection of Future Climate Scenarios

Contents 2.1 General Circulation Model 2.2 Downscaling 2.3 Climate Scenarios 2.4 Uncertainty of Climate Scenarios

Purposes What are climate scenarios? Why do we need climate scenarios? How can we setup our climate scenarios? Current climate scenario? Future climate scenario?

What are climate scenarios? Climate scenarios represent possible weather statistics, which may consist of monthly mean rainfall, Probability of wet day (rainy day) monthly mean temperature, Standard deviation of temperature for each month …

Why? Those studies related to climate require weather data as inputs. However, future weather data are not available, unless you have a time machine. Thus, we need to project possible future climate scenarios. Then, possible future weather can be generated based on these scenarios.

Model Daily Rainfall Q=ϕ✕R Q: Daily stream flow R: Daily rainfall ϕ : stream flow index Daily Stream flow

How? Future climate scenarios can be derived based on the outputs of GCMs and current climate scenarios. Past and current weather data are recorded by weather stations, and thus current climate scenarios can be determined. Current climate scenarios are modified based on the climate projections of GCMs to form future climate scenarios.

General Circulation Models (GCMs) can produce daily weather data, but the daily data are not used directly. Instead, they are used to setup climate scenarios. GCMs have better resolutions in recent years, but there is still space for further improvement on the ability of climate projection for a local area.

The Risk Assessment Procedure 全球環流模式 GCM Projections Risk Information for Decision Making 降尺度分析 Downscaling 評估模式 Assessment Model 氣候情境 Climate Scenarios 氣象資料合成 Weather Generation

How to Set up Climate Scenarios The most import step on an impact study is to setup climate change scenarios. There are four methods to define climate scenarios. Based on GCMs’ projections Assumptions T=+2 oC; +4 oC P= 0%, 10%, 20% Spatial Analog Such as future climate in Taiwan may be similar to current climate in Philippines. Temporal Analog Assuming future climate may be repeated as climate in a specified period in the past. Because we don’t have future weather data, the first step of impact assessment is to setup climate scenarios. Questions are how we can do since we don’t have future weather data. There are four possible methods for defining climate scenarios.

Only the first type of scenario can reflect the physical characteristics of enhanced greenhouse effects and man-induced global warming . Different seasons may have different changes in climate. Moreover, there is difference between night and day time. Such difference can only be reasonably provided by GCMs. Thus, currently most of the impact assessments are done based on climate scenarios derived from GCMs’ predictions.

2.1 General Circulation Models

General Circulation Model A general circulation model (GCM) is a mathematical model of the general circulation of a planetary atmosphere or ocean and based on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for complex computer programs commonly used for simulating the atmosphere or ocean of the Earth. Atmospheric and oceanic GCMs (AGCM and OGCM) are key components of global climate models along with sea ice and land-surface components. GCMs and global climate models are widely applied for weather forecasting, understanding the climate, and projecting climate change. ---From Wikipedia

Global Atmospheric Model Climate models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. To “run” a model, scientists divide the planet into a 3-dimensional grid, apply the basic equations, and evaluate the results. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points. -From Wikipedia

How GCM works? GCM Grids GCMs divide earth into many layers of grids. GCMs provide forecasts for each grid point, which represent average values for a grid.

GCM Resolutions Model Country AGCM OGCM CCCM Canada T32L10 1.8o 1.8o L29 GFDL USA R30L14 2.0o2.0o L18 GISS 4o5o L9 4o5o L13 UKMO UK 2.5o3.8o L19 2.5o3.8o L20 The table lists the resolutions of different GCMs. Most of climate simulations are done by using coupled GCMs, including atmospheric GCM (AGCM) and Oceanic GCM (OGCM). Fluxes of energy, water vapor, CO2, etc. are considered to link AGCM to OGCM. One degree of longitude or latitude is around 100 km. Thus, a grid size of GISS AGCM is 400km500km.

Spatial Scale of Taiwan Main Island 基隆25008 121.44 Taiwan：3o1.2o   GISS ：4o5o 梧棲 How about Taiwan? The ranges of longitude and latitude of Taiwan area are 22o ~25o08 and 120.18~121.36, respectively. 花蓮121.36 高雄 120.18 恆春 220

The red cross mark is grid points in GSM, one GCM The red cross mark is grid points in GSM, one GCM. There is only one grid located in Taiwan. （台灣大學吳明進教授提供）

GCMs provide projections for each grid point, which represent average values for a grid. The risk studies for water resources or ecosystems often require climate projections for a smaller area and may need daily weather data. Thus, as mentioned, spatial and temporal downscaling processes are necessary.

2.2 Downscaling

Definition of Downscaling Downscaling is a technique to obtain information for a finer scale from information for a larger scale. For example, temporal downscaling can provide daily data based on monthly means. Weather generation is kind of temporal downscaling. On the other hand, spatial downscaling can produce data for a local area, such as a watershed, from data for a regional area, such as an island or a state.

Known areal average to find local characteristics GCM Scale 250 km250km Upstream Watershed Scale 25 km25km Ecosystem 1 km1 km 250 km 25 km 250 km

Downscaling How to find its climate?

How to Apply GCMs’ Outputs? How can we setup climate scenarios? Average predictions for the four nearest grids? Or, taking predictions from the nearest grid point? Or, what else? Grids that GCMs provide projections

Spatial Downscaling Methods Simple Downscaling (Delta Method) Climate changes of a local area are assumed the same as the nearest grid point Modifying recorded weather data by imposing the predicted climate changes of the nearest grid. Modifying historic weather statistics based on climate change forecasts of the nearest grid. Statistical Downscaling Finding the statistical relationships between regional climate and local climate. Physical Downscaling Taking GCMs’ forecasts as boundary conditions for a regional climate model. There are three methods adopted to downscale climate predictions from a regional scale to a local scale.

Method 1.1 Modifying recorded weather data by imposing the predicted climate changes It assumes there is uniform climate changes within a grid and observed weather sequence is repeatable. The days in the same month are modified by the same changes even though they are in different years.

1. T1ocal = Tregional 2. RPprecip-1ocal = RPprecip- regional

Method 1.2 Modifying historic weather statistics based on climate change projections and then generating weather data. It also assumes there is uniform climate changes within a grid, e.g. the change of a weather station is the same as the change predicted by the nearest grid point. Monthly changes predicted by GCMs are used to modify historical monthly weather statistics. Modified monthly statistics are future climate scenarios and are applied to generate future weather data.

Method 2 Statistical Downscaling First, finding the relationships between regional climate pattern and local climate. Then, monthly changes predicted by GCMs are projected to a local station based on the identified relationships. What relationships can be found? How?

Recorded Rainfall FROM :21-OCT-2005 00:00 TO :21-OCT-2005 08:30 Rank Rainfall (mm) station Code Location 1 40.0 冬 山 C1U68 宜蘭縣冬山鄉(冬山國中) 2 28.5 竹 子 湖 46693 台北市陽明山(氣象站) 3 27.0 01A42 台北市陽明山(十河局) 4 24.5 泰 平 C0A55 台北縣雙溪鄉 5 24.0 寒 溪 C1U67 宜蘭縣冬山鄉(大進國小) 6 23.5 玉 蘭 C0U65 宜蘭縣大同鄉 7 太 平 L1A84 台北縣雙溪鄉(翡翠水庫) 8 18.0 三 星 C1U66 宜蘭縣三星鄉(三星鄉運動公園) 9 13.5 牛 鬥 C1U50 10 13.0 北投國小 A1A9V 台北市北投區(養工處)

Regional Climate Local Climate Historical Weather Data Regional Climate Regional Weather Pattern GCMs’ Predicted Regional Weather Pattern We meet a problem that we need to retrieve local climate information from known regional climate information. Question is how we can do? Downscaling Relationship Projected Local Weather Data Local Weather Data Local Climate

Similar Pattern, but different Weather 10.21.2005 Rainy Day 10.28.2005 Sunny Day

Method 2 Bias Correction Assume the probability of observed data is the same as the probability of GCM projections. CDF CDF OBS GCM

Example of Bias Correction GFCM21_Jan

Bias Correction of GCM projections

Bias Correction for Temperature

Bias Correction for Precipitation

Bias Correction for Climate Variable where μLocal,F,m is the bias-corrected future mean value of month m for a local area, μGCM,F,m and μGCM,m are GCM projected future and current mean values, σGCM,m is the GCM projected standard deviation of month m under current climate condition, μobserved,m and σobserved,m are the observed mean and standard deviation of month m under current climate condition for a local area.

Example of Bias Correction

Generalized Bias-Correction Method Climate variable X belongs to distribution F(X, α, β) where α and β are parameters of distribution function. F(X, αGCM, βGCM) F(X, αobs, βobs) Prob Prob CDF CDF Xunbias XGCM

Method 3 physical downscaling Taking GCMs’ forecasts as boundary conditions for a regional climate model. Prof. Wu in AS of NTU uses the outputs from Global Spectral Model (GSM)_CCM3 as boundary conditions to drive Regional Spectral Model (RSM). Other regional models, including Purdue Model and MM5, are used in Taiwan to produce a local climate scenario matrix.

資料來源：tccip.ncdr.nat.gov.tw

資料來源：台大吳明進教授

GSM & RSM Resolutions Model Resolutions GSM: Global Spectral Model GSM or RSM0 280km 280km RSM1 50km 50km RSM2 15km 15km GSM: Global Spectral Model RSM: Regional Spectral Model

Weather Research and Forecasting (WRF) Model The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting needs. TCCIP project used WRF model to downscale MRI projections to the scale of 5km.

2.3 Climate Scenarios

Procedure of Risk Assessment

Emission Scenarios vs Climate Scenarios Outputs: Daily & Monthly Data Stabilization at 550 ppm GCMs Climate Scenarios: Current Scenarios Future Scenarios Emission Scenarios

Emission Scenarios （溫室氣體排放情境） SRES Scenarios （Special Report on Emissions Scenarios, 2000） RCPs Scenarios (Representative Concentration Pathways) radiative forcing values in the year 2100 relative to pre-industrial values (+2.6, +4.5, +6.0, and +8.5 W/m2, respectively) Climate Scenarios （氣候情境） Current Climate Scenario （Baseline） Future Climate Scenario

Experiments of GCMs Equilibrium Experiment Transition Experiment Setup initial atmospheric conditions Running model to reach equilibrium states Change climate parameters, e.g.2×CO2 Re-running model to reach another equilibrium states Comparisons between two equilibrium states   Transition Experiment Respond to instant forcing Respond to gradual change of CO2 concentration Gradually increase CO2 concentration Simulate climate We are talking about using GCMs’ predictions to setup climate scenarios, but what kinds of GCM data are available? There are two types of data sets which are produced based on equilibrium and transition experiments, respectively. We will introduce these two types of datasets in more detail in the followings.

SRES Scenarios SRES is defined based on future economic growth toward global or regional Economic or environmental SRES classifies four scenarios, including A1, A2, B1,and B2. B1

IPCC AR4 Popular Scenarios A1B B1 A2 A1B B1 Stabilization at 550 ppm

RCPs RCP8.5 Rising radiative forcing pathway leading to 8.5 W/m2 in 2100. RCP6.0 Stabilization without overshoot pathway to 6 W/m2 at stabilization after 2100 RCP4.5 Stabilization without overshoot pathway to 4.5 W/m2 at stabilization after 2100 RCP2.6 Peak in radiative forcing at ~ 3 W/m2 before 2100 and decline

Database IPCC Data Distribution Center http://www.ipcc-data.org/index.html TCCIP also provides scenarios for Taiwan. http://tccip.ncdr.nat.gov.tw/NCDR/main/index.aspx

AR5採用GCMs - 由CMIP5專案彙整 來自28個單位，共計61個GCMs ↑

TCCIP Climate Scenarios TCCIP [Taiwan Climate Change Projection and Information Platform Project: 臺灣氣候變遷推估與資訊平台計畫] is a core project funded by National Science Council, which is in charge of preparing climate scenarios for climate change impact study in Taiwan. Climate scenarios are developed for four periods. Current Climate (Baseline, 基期) : 1986~2005 Short-term Future Climate （短期） : 2020~2039 Mid-term Future Climate （中期） : 2050~2069 Long-term Future Climate （長期） : 2070~2099

GCMs對應各RCP情境整理表(1/2) Modeling ACenter Model RCP2.6 RCP4.5 RCP6.0 Baseline  BCC BCC-CSM1.1   BCC-CSM1.1(m)  CCCma CanCM4 CanESM2  CMCC CMCC-CESM    CMCC-CM CMCC-CMS  CNRM-CERFACS CNRM-CM5 CNRM-CM5-2  CSIRO-BOM ACCESS1.0 ACCESS1.3  CSIRO-QCCCE CSIRO-Mk3.6.0  EC-EARTH EC-EARTH  FIO FIO-ESM  GCESS BNU-ESM  INM INM-CM4  IPSL IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR  LASG-CESS FGOALS-g2  LASG-IAP FGOALS-s2  MIROC MIROC4h AMIROC5 MIROC-ESM MIROC-ESM-CHEM  MOHC HadCM3 HadGEM2-A HadGEM2-CC HadGEM2-ES

GCMs對應各RCP情境整理表(2/2) Modeling ACenter Model RCP2.6 RCP4.5 RCP6.0 Baseline  MPI-M MPI-ESM-LR   MPI-ESM-MR MPI-ESM-P  MRI MRI-CGCM3 MRI-ESM1  NASA GISS GISS-E2-H GISS-E2-H-CC GISS-E2-R GISS-E2-R-CC  NCAR CCSM4  NCC NorESM1-M NorESM1-ME  NIMR/KMA HadGEM2-AO  NOAA GFDL GFDL-CM2.1 GFDL-CM3 GFDL-ESM2G GFDL-ESM2M  NSF-DOE-NCAR CESM1(BGC) CESM1(CAM5)   CESM1(CAM5.1, FV2) CESM1(FASTCHEM) CESM1(WACCM)

How many GCMs should be chosen? How to choose GCMs?

Procedure to Choose GCMs Climate Zonation Analysis Performance of GCMs for each station Weighted performance for a zone Ranking GCMs Choice of GCMs

Climate Zonation Those which GCMs could provide reasonable baseline climate have higher priority for risk study. However, it is not realistic that nearby weather stations choose different GCMs, especially within the same watershed. Therefore, climate zonation is determined first. The weather stations in the same zone should use the same GCMs.

CWB weather stations 共25站 站名 經度 緯度 淡水 121.83 25.17 鞍部 121.73 25.19 臺北 121.51 25.04 竹子湖 121.54 基隆 25.13 彭佳嶼 122.07 25.63 花蓮 121.61 23.98 蘇澳 121.86 24.60 宜蘭 121.75 24.77 東吉島 119.66 23.26 澎湖 119.56 23.57 臺南 120.23 23.01 高雄 120.31 22.57 嘉義 120.42 23.50 臺中 120.68 24.15 阿里山 120.81 23.51 大武 120.90 22.37 玉山 120.95 23.49 新竹 121.01 24.83 恆春 120.74 22.01 成功 121.37 23.10 蘭嶼 121.55 22.04 日月潭 23.88 臺東 121.15 22.75 梧棲 120.52 24.26 共25站

林嘉佑（2014） 劉子明（2010） 吳明進（1993） 北部 台北、淡水、新竹、梧棲、台中、 台北、淡水、新竹 台北、新竹、彭佳嶼 北海岸 基隆 東北海岸 東部 宜蘭、花蓮 成功、台東 東北部 宜蘭 花蓮、成功、台東 花蓮、成功、台東、大武、恆春 南部 大武、恆春 恆春半島 西南部 嘉義、台南、高雄 西部 台中、高雄、台南 西南 澎湖、台南、高雄 北部山區 鞍部、竹子湖、蘇澳 竹子湖、鞍部 中部山區 日月潭、玉山 中部 台中、日月潭 南部山區 阿里山 山地 北部外島 彭佳嶼 西部外島 澎湖、東吉島 東部外島 蘭嶼

針對氣象站挑選GCMs鄰近格點 GCM 採用站點 對應測站 bcc-csm1-1-m MRI-CGCM3 119.25、22.991 東吉島   119.25、22.991 東吉島 119.25、24.112 澎湖 120.375、21.869 大武、恆春 120.375、22.991 台南、高雄、嘉義、阿里山 120.375、24.112 台中、日月潭、梧棲 121.5、21.869 蘭嶼 121.5、22.991 玉山、成功、台東 121.5、24.112 花蓮、蘇澳 121.5、25.234 淡水、鞍部、台北、竹子湖、基隆、宜蘭、新竹 122.625、25.234 彭佳嶼 bcc-csm1-1 120.938、23.72 臺北、花蓮、蘇澳、宜蘭、東吉島、澎湖、臺南、高雄、嘉義、臺中、阿里山、大武、玉山、新竹、恆春、成功、蘭嶼、日月潭、臺東、梧棲 120.938、26.511 淡水、鞍部、竹子湖、基隆、彭佳嶼

Performance Indicator Correlation of mean monthly rainfall（越高越好） RMSE of mean monthly rainfall in dry season (越低越好) RMSE of mean monthly rainfall in wet season (越低越好) Rank based on the three indicators Rank based on each indicator Sum of ranks of three indicators Determine final rank R Dry RMSR Wet RMSE Total Final Rank GCM A 2 1 3 6 GCM B 4 GCM C 9 GCM D 11

Performance for a zone Sum of final ranks of all stations in the zone Rank again 淡水 台北 台中 新竹 梧棲 Sum Final bcc-csm1-1-m 14 12 3 11 43 bcc-csm1-1 1 8 20 10 19 58 CCSM4 5 2 31 CESM1-CAM5 4 6 16 9 47 CSIRO-Mk3-6-0 32 FIO-ESM 17 51 GFDL-CM3 18 57 GFDL-ESM2G 15 78 GFDL-ESM2M 13 80 GISS-E2-H 70 GISS-E2-R 7 49 HadGEM2-AO IPSL-CM5A-LR 62 IPSL-CM5A-MR MIROC-ESM-CHEM 86 MIROC-ESM 82 MIROC5 37 MRI-CGCM3 NorESM1-M 41 NorESM1-ME 33

Choice of GCMs 部分氣候分區僅包含單一測站，排序結果與單站分析結果相同 rank 西北部 東部 恆春半島 南部 北部山區 中部山區 西部離島 台灣 1 HadGEM2-AO CESM1-CAM5 MIROC5 bcc-csm1-1 2 CCSM4 GISS-E2-R 3 CSIRO-Mk3-6-0 bcc-csm1-1-m NorESM1-ME 4 5 MRI-CGCM3 部分氣候分區僅包含單一測站，排序結果與單站分析結果相同 為確保區域氣象站之推薦模式亦可反映出台灣整體之氣候特性，部分僅於分區內表現較佳之模式已被剔除

2.4 Uncertainty of GCM projections

Relationships between records of a local weather station and predictions for the nearest grid point SRES Country Study Program CGCM1 CSIRO HADCM3 CCCM GFDL GISS Taipei Rainfall 0.68 -0.68 0.69 0.72 -0.36 0.91 Temp. 0.98 0.99 0.97 TaiChong 0.74 -0.80 0.86 0.76 -0.21 0.87 1.00 0.95 Tainan 0.85 -0.81 0.59 -0.26 0.93 TaiDong 0.80 -0.57 0.84 -0.60 0.39 0.94

Comparisons between different grids’Projections SRES-CGCM2 A2

Summary Climate scenarios, in fact, are weather statistics. Current climate scenarios can be determined based on historical weather data. Climate change scenarios may be derived from GCMs or just simple assumptions. Then, future climate scenarios can be designed. Future weather data could be generated based on climate statistics or simply impose changes on current records. Using more than one GCM is recommended to avoid the effects of model bias.

What should you know? What are SRES and RCPs scenarios? Why should you downscale GCMs’ outputs? What are climate scenarios? How can you downscale GCMs’ outputs to setup climate change scenarios? How can you prepare your weather data for your risk assessment model?